Collaborative Interest-aware Graph Learning for Group Identification

• URL: http://arxiv.org/abs/2506.14826v1
• Date: Fri, 13 Jun 2025 11:15:43 GMT
• Title: Collaborative Interest-aware Graph Learning for Group Identification
• Authors: Rui Zhao, Beihong Jin, Beibei Li, Yiyuan Zheng,
• Abstract summary: An increasing number of users are joining group activities on online social platforms.<n>We propose CI4GI, a Collaborative Interest-aware model for Group Identification.<n>The results of experiments on three real-world datasets demonstrate that CI4GI significantly outperforms state-of-the-art models.
• Score: 8.099443043148886
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: With the popularity of social media, an increasing number of users are joining group activities on online social platforms. This elicits the requirement of group identification (GI), which is to recommend groups to users. We reveal that users are influenced by both group-level and item-level interests, and these dual-level interests have a collaborative evolution relationship: joining a group expands the user's item interests, further prompting the user to join new groups. Ultimately, the two interests tend to align dynamically. However, existing GI methods fail to fully model this collaborative evolution relationship, ignoring the enhancement of group-level interests on item-level interests, and suffering from false-negative samples when aligning cross-level interests. In order to fully model the collaborative evolution relationship between dual-level user interests, we propose CI4GI, a Collaborative Interest-aware model for Group Identification. Specifically, we design an interest enhancement strategy that identifies additional interests of users from the items interacted with by the groups they have joined as a supplement to item-level interests. In addition, we adopt the distance between interest distributions of two users to optimize the identification of negative samples for a user, mitigating the interference of false-negative samples during cross-level interests alignment. The results of experiments on three real-world datasets demonstrate that CI4GI significantly outperforms state-of-the-art models.

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